I am using version 8.2
What I am trying to do is to isolate normally distributed variables closest
to a mean of 50 and a sd of 10.
The following clip is from a generation of 1000 such variables . 76 and 833
are variables that qualify to the relevant criteria .They are saved in my
directory as a76.dta and a833.dta respectively .
mean sd i
> > ---------------------------
> > 1. 49.96996 9.978514 76
......................
> > 15. 50.08827 9.936735 833
What I need is a code that is capable of (for example) , renaming a76.dta
, b76.dta & a833.dta , b833.dta , and thus being able to freely write
over the previous a1.dta , a2.dta ..... a1000.dta , to handle other
criteria , while conserving memory .
Victor Michael Zammit
----- Original Message -----
From: Nick Cox <[email protected]>
To: <[email protected]>
Sent: Saturday, September 11, 2004 4:10 PM
Subject: st: RE: Re:Is there any way ?
> I am a bit lost on what you are trying to do
> here. You seem to be generating samples from
> a normal, but only wanting to keep them if they appear
> really close to the target population
> in the sense that sample mean and sample sd
> are really close to the corresponding population
> parameters.
>
> If that's right and memory is the issue, then
> you should test each sample just after generation
> and drop it immediately if it is not up to your
> standards. Or equivalently you could do something
> like this:
>
> gen prov = .
> local j = 1
> qui while `j' < 100
> replace prov = 50 + 10 * invnorm(uniform())
> su prov
> if inrange(r(mean),49.9,50.1)) & inrange(r(sd),9.9,10.1)
> gen OK`j++' = prov
> }
> }
>
> That way, what you have in memory is just
> the last sample generated
> and all previous samples generated deemed OK
>
> By the way, standard advice in the Statalist FAQ is
>
> The current version of Stata is 8.2. Please specify if
> you are using an earlier version; otherwise, the answer
> to your question is likely to refer to commands or
> features unavailable to you.
>
> Nick
> [email protected]
>
> P.S. Stata is telling you why your code didn't work. First
> time round it thinks that the
>
> [1]
>
> in
>
> use ai[1].dta, clear
>
> is a reference to weights. This also is puzzling. Presumably
> you wouldn't try to -use- that dataset if you hadn't previously
> saved it, but is that a legal filename with your (unstated)
> operating system?
>
> --------------------------------------------------------------
>
> victor michael zammit
>
> > the following dataset lists the 16 variables , out of 1000 normally
> > distributed variable with mean 50 and standard deviation 10
> > , and then <
> > keep if mean>49.9 & mean < 50.1 & sd > 9.9 & sd< 10.1 > . I
> > would like to
> > do this for different < set obs # > ,and 10,000 instead of
> > 1000 , but
> > memory becomes an issue .What I thought I could do , is to
> > save variable i
> > ' s under a different name, and then go to the next <set
> > obs #> and <
> > save,replace> over the previous 1000 or whatever variables .
> >
> > +---------------------------+
> > mean sd i
> > ---------------------------
> > 1. 49.96996 9.978514 76
> > 2. 49.92636 10.07861 118
> > 3. 49.97271 10.07149 122
> > 4. 50.07562 10.07239 218
> > 5. 49.94316 9.913686 220
> > ---------------------------
> > 6. 50.02873 10.05811 346
> > 7. 50.01596 10.04384 442
> > 8. 50.00026 9.943673 513
> > 9. 49.91186 9.95947 533
> > 10. 50.00311 10.00525 629
> > ---------------------------
> > 11. 50.09705 9.929688 653
> > 12. 49.98079 10.04592 678
> > 13. 49.93727 9.962771 686
> > 14. 49.98563 10.07247 694
> > 15. 50.08827 9.936735 833
> > ---------------------------
> > 16. 49.97584 10.05994 854
> >
> >
> > I experimented with a loop :
> >
> >
> > . local i = 1
> >
> > . while `i'<= _N {
> > 2. use ai[`i'].dta,clear
> > 3. save b`i',replace
> > 4. local i = `i'+1
> > 5. }
> > weights not allowed
> > r(101);
> >
> > but it did not work
> > Is there a way I could do this ?
>
> *
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>
*
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